Dataset Information

A Smartphone-Based Crowd-Sourced Database for Environmental Noise Assessment.

ABSTRACT: Noise is a major source of pollution with a strong impact on health. Noise assessment is therefore a very important issue to reduce its impact on humans. To overcome the limitations of the classical method of noise assessment (such as simulation tools or noise observatories), alternative approaches have been developed, among which is collaborative noise measurement via a smartphone. Following this approach, the NoiseCapture application was proposed, in an open science framework, providing free access to a considerable amount of information and offering interesting perspectives of spatial and temporal noise analysis for the scientific community. After more than 3 years of operation, the amount of collected data is considerable. Its exploitation for a sound environment analysis, however, requires one to consider the intrinsic limits of each collected information, defined, for example, by the very nature of the data, the measurement protocol, the technical performance of the smartphone, the absence of calibration, the presence of anomalies in the collected data, etc. The purpose of this article is thus to provide enough information, in terms of quality, consistency, and completeness of the data, so that everyone can exploit the database, in full control.

SUBMITTER: Picaut J

PROVIDER: S-EPMC8345695 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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A Smartphone-Based Crowd-Sourced Database for Environmental Noise Assessment.

Picaut Judicaël J Boumchich Ayoub A Bocher Erwan E Fortin Nicolas N Petit Gwendall G Aumond Pierre P

International journal of environmental research and public health 20210722 15

Noise is a major source of pollution with a strong impact on health. Noise assessment is therefore a very important issue to reduce its impact on humans. To overcome the limitations of the classical method of noise assessment (such as simulation tools or noise observatories), alternative approaches have been developed, among which is collaborative noise measurement via a smartphone. Following this approach, the NoiseCapture application was proposed, in an open science framework, providing free a ...[more]

PMID: 34360073

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Project description:BackgroundVideo-based assessment by experts may structurally measure surgical performance using procedure-specific competency assessment tools (CATs). A CAT for minimally invasive esophagectomy (MIE-CAT) was developed and validated previously. However, surgeon's time is scarce and video assessment is time-consuming and labor intensive. This study investigated non-procedure-specific assessment of MIE video clips by MIE experts and crowdsourcing, collective surgical performance evaluation by anonymous and untrained laypeople, to assist procedure-specific expert review.MethodsTwo surgical performance scoring frameworks were used to assess eight MIE videos. First, global performance was assessed with the non-procedure-specific Global Operative Assessment of Laparoscopic Skills (GOALS) of 64 procedural phase-based video clips < 10 min. Each clip was assessed by two MIE experts and > 30 crowd workers. Second, the same experts assessed procedure-specific performance with the MIE-CAT of the corresponding full-length video. Reliability and convergent validity of GOALS for MIE were investigated using hypothesis testing with correlations (experience, blood loss, operative time, and MIE-CAT).ResultsLess than 75% of hypothesized correlations between GOALS scores and experience of the surgical team (r < 0.3), blood loss (r = - 0.82 to 0.02), operative time (r = - 0.42 to 0.07), and the MIE-CAT scores (r = - 0.04 to 0.76) were met for both crowd workers and experts. Interestingly, experts' GOALS and MIE-CAT scores correlated strongly (r = 0.40 to 0.79), while crowd workers' GOALS and experts' MIE-CAT scores correlations were weak (r = - 0.04 to 0.49). Expert and crowd worker GOALS scores correlated poorly (ICC ≤ 0.42).ConclusionGOALS assessments by crowd workers lacked convergent validity and showed poor reliability. It is likely that MIE is technically too difficult to assess for laypeople. Convergent validity of GOALS assessments by experts could also not be established. GOALS might not be comprehensive enough to assess detailed MIE performance. However, expert's GOALS and MIE-CAT scores strongly correlated indicating video clip (instead of full-length video) assessments could be useful to shorten assessment time.

Project description:IntroductionTo mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and result in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies.MethodsWe used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions.ResultsMobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels.ConclusionUsing crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.

Dataset Information

A Smartphone-Based Crowd-Sourced Database for Environmental Noise Assessment.

Publications

A Smartphone-Based Crowd-Sourced Database for Environmental Noise Assessment.

Similar Datasets

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